This invention relates generally to medical imaging systems, and more specifically to a method and apparatus for soft-tissue volume visualization using a medical imaging system.
In spite of recent advancements in computed tomography (CT) technology, such as faster scanning speed, larger coverage with multiple detector rows, and thinner slices, energy resolution is still a missing piece, namely, a wide x-ray photon energy spectrum from the x-ray source and a lack of energy resolution from CT detection systems preclude energy discrimination CT.
X-ray attenuation through a given object is not a constant. Rather, x-ray attenuation is strongly dependent on the x-ray photon energy. This physical phenomenon manifests itself in an image as a beam-hardening artifact, such as non-uniformity, shading, and streaks. Some beam-hardening artifacts can be easily corrected, but others may be more difficult to correct. In general, known methods to correct beam hardening artifacts include water calibration, which includes calibrating each CT machine to remove beam hardening from materials similar to water, and iterative bone correction, wherein bones are separated in the first-pass image then correcting for beam hardening from bones in the second-pass. However, beam hardening from materials other than water and bone, such as metals and contrast agents, may be difficult to correct. In addition, even with the above described correction methods, conventional CT does not provide quantitative image values. Rather, the same material at different locations often shows different CT numbers.
Another drawback of conventional CT is a lack of material characterization. For example, a highly attenuating material with a low density can result in the same CT number in the image as a less attenuating material with a high density. Thus, there is little or no information about the material composition of a scanned object based solely on the CT number.
Additionally, similar to traditional x-ray methods, at least some known soft-tissue volume visualization methods project rays through an object. However, without segmenting out bone from other material within the object, visualization of subtle, yet possibly diagnostically important, structures may be difficult. Traditionally, bone segmentation of CT images is based on image characteristics and Hounsfield numbers. Dual-energy decomposition lends itself nicely for the soft-tissue and bone separation. However, the methods and systems described below can also remove calcification, which contains diagnostic information in CT.